Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer
This addresses fairness and accuracy issues in collaborative human-AI systems, though it is incremental as it builds on rejection learning.
The paper tackles the problem of bias and accuracy in multi-agent decision-making systems by introducing 'learning to defer', a framework that allows automated models to pass decisions to external agents. Experiments show this approach improves both accuracy and fairness, even with biased users.
In many machine learning applications, there are multiple decision-makers involved, both automated and human. The interaction between these agents often goes unaddressed in algorithmic development. In this work, we explore a simple version of this interaction with a two-stage framework containing an automated model and an external decision-maker. The model can choose to say "Pass", and pass the decision downstream, as explored in rejection learning. We extend this concept by proposing "learning to defer", which generalizes rejection learning by considering the effect of other agents in the decision-making process. We propose a learning algorithm which accounts for potential biases held by external decision-makers in a system. Experiments demonstrate that learning to defer can make systems not only more accurate but also less biased. Even when working with inconsistent or biased users, we show that deferring models still greatly improve the accuracy and/or fairness of the entire system.